Given an input dataset, we use SMACdown to find the best parameters for creating an ensemble from that dataset.

The script uses as inputs, beside the identifier of the dataset, the evaluation metric we maximize (defaulting to average_phi), the objective field and a string used as a prefix when naming intermediate resources created by the workflow. You can select the metric to optimize (see below).

Classification metrics:

average_recall

average_phi

accuracy

average_precision

average_f_measure

Regression metrics:

r_squared

mean_absolute_error

mean_squared_erro

This workflow will generate a big number of auxiliary resources when executed. To instruct the script to delete all of them before finishing set the delete-resources execution input parameter to true.

Given an input dataset, we use SMACdown to find the best parameters for creating an ensemble from that dataset.

The script uses as inputs, beside the identifier of the dataset, the evaluation metric we maximize (defaulting to average_phi), the objective field and a string used as a prefix when naming intermediate resources created by the workflow. You can select the metric to optimize (see below).

Classification metrics:

average_recall

average_phi

accuracy

average_precision

average_f_measure

Regression metrics:

r_squared

mean_absolute_error

mean_squared_erro

This workflow will generate a big number of auxiliary resources when executed. To instruct the script to delete all of them before finishing set the delete-resources execution input parameter to true.

Given an input dataset, we use SMACdown to find the best parameters for creating an ensemble from that dataset.

The script uses as inputs, beside the identifier of the dataset, the evaluation metric we maximize (defaulting to average_phi), the objective field and a string used as a prefix when naming intermediate resources created by the workflow. You can select the metric to optimize (see below).

Classification metrics:

average_recall

average_phi

accuracy

average_precision

average_f_measure

Regression metrics:

r_squared

mean_absolute_error

mean_squared_erro

This workflow will generate a big number of auxiliary resources when executed. To instruct the script to delete all of them before finishing set the delete-resources execution input parameter to true.